Principle Azure Data Solution Engineer

Ability to guide customers through data platform modernization decisions, balancing architecture, governance, cost, and performance considerations to enable AI‑ready and enterprise‑scale outcomes. Solid technical foundation designing and modernizing Cloud & AI solutions on Azure, partnering with customers to move from legacy environments to secure, scalable cloud native architectures Ability to lead technical migration and modernization discussions, applying structured approaches (e.g., 6R strategy) to guide customer decisions. Experience influencing technical decision makers (architects, platform leads, engineering managers) by translating complex architecture into clear, defensible solutions. Solid understanding of hybrid and cloud native architectures, including networking fundamentals (virtual networks, secure connectivity, routing, performance considerations). Knowledge of Azure security and compliance principles, including identity, networking security, data protection, and alignment to regulatory and compliance frameworks. Handson technical mindset with the ability to design, validate, and explain architectures, not just describe services. Proven collaboration skills working with customers, partners, engineering teams, and account stakeholders to deliver outcomes. Drive technical sales by using technical demos, proof of concepts, technical architecture accelerator to influence solution design and enable deployments. Lead architecture sessions and technical workshops to accelerate Cloud & AI adoption. Build trusted relationships with platform leads to co-design secure, scalable solutions. Resolve technical blockers by collaborating with engineering and sharing customer insights. Azure Data (L300 vs L400) Hands‑on experience modernizing data platforms and analytics workloads on Azure, including Lakehouse and modern data warehouse architectures, legacy EDW/Hadoop modernization, and migration to Fabric‑centric solutions. Solid understanding of end‑to‑end data architecture concepts, including ingestion (batch and streaming), storage, transformation, analytics, performance optimization, security, and data governance. Experience building and operating data engineering pipelines across Fabric and Databricks to support analytics, real‑time intelligence, and AI/ML workloads. Ability to guide customers through data platform modernization decisions, balancing architecture, governance, cost, and performance considerations to enable AI‑ready and enterprise‑scale outcomes. Bachelor's Degree in Computer Science, Information Technology, Engineering or related field AND 6+ years technical pre-sales or technical consulting experience OR equivalent experience. Advanced understanding of Cloud & AI services, including security, compliance, and hybrid cloud scenarios. Ability to engage technical and business stakeholders to design and deploy scalable, secure cloud solutions. 4+ years' experience with cloud and hybrid, or on premises infrastructure, architecture designs, migrations, industry standards, and/or technology management Familiarity with enterprise platforms such as SAP, Oracle, and ISVs like NetApp, VMware, and RedHat. Knowledge of regulatory frameworks such as GDPR, HIPAA, and Public Sector compliance standards. Build strategic influence by shaping infrastructure, data, and application modernization decisions for AI driven business workloads